{"id":"https://openalex.org/W4400490420","doi":"https://doi.org/10.1109/civemsa58715.2024.10586572","title":"Volumetric Hippocampus Segmentation Using 3D U-Net Based On Transfer Learning","display_name":"Volumetric Hippocampus Segmentation Using 3D U-Net Based On Transfer Learning","publication_year":2024,"publication_date":"2024-06-14","ids":{"openalex":"https://openalex.org/W4400490420","doi":"https://doi.org/10.1109/civemsa58715.2024.10586572"},"language":"en","primary_location":{"id":"doi:10.1109/civemsa58715.2024.10586572","is_oa":false,"landing_page_url":"https://doi.org/10.1109/civemsa58715.2024.10586572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100497329","display_name":"Ramadhan Sanyoto Sugiharso Widodo","orcid":null},"institutions":[{"id":"https://openalex.org/I166843116","display_name":"Sepuluh Nopember Institute of Technology","ror":"https://ror.org/05kbmmt89","country_code":"ID","type":"education","lineage":["https://openalex.org/I166843116"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Ramadhan Sanyoto Sugiharso Widodo","raw_affiliation_strings":["Institut Teknologi Sepuluh Nopember,Faculty of Intelligent Electrical and Informatics Technology,Department of Electrical Engineering, Department of Computer Engineering,Surabaya,Indonesia,60111"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institut Teknologi Sepuluh Nopember,Faculty of Intelligent Electrical and Informatics Technology,Department of Electrical Engineering, Department of Computer Engineering,Surabaya,Indonesia,60111","institution_ids":["https://openalex.org/I166843116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103152092","display_name":"I Ketut Eddy Purnama","orcid":"https://orcid.org/0000-0002-7438-7880"},"institutions":[{"id":"https://openalex.org/I166843116","display_name":"Sepuluh Nopember Institute of Technology","ror":"https://ror.org/05kbmmt89","country_code":"ID","type":"education","lineage":["https://openalex.org/I166843116"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"I Ketut Eddy Purnama","raw_affiliation_strings":["Institut Teknologi Sepuluh Nopember,Faculty of Intelligent Electrical and Informatics Technology,Department of Electrical Engineering, Department of Computer Engineering,Surabaya,Indonesia,60111"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institut Teknologi Sepuluh Nopember,Faculty of Intelligent Electrical and Informatics Technology,Department of Electrical Engineering, Department of Computer Engineering,Surabaya,Indonesia,60111","institution_ids":["https://openalex.org/I166843116"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084212006","display_name":"Reza Fuad Rachmadi","orcid":"https://orcid.org/0000-0001-9101-5598"},"institutions":[{"id":"https://openalex.org/I166843116","display_name":"Sepuluh Nopember Institute of Technology","ror":"https://ror.org/05kbmmt89","country_code":"ID","type":"education","lineage":["https://openalex.org/I166843116"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Reza Fuad Rachmadi","raw_affiliation_strings":["Institut Teknologi Sepuluh Nopember,Faculty of Intelligent Electrical and Informatics Technology,Department of Electrical Engineering, Department of Computer Engineering,Surabaya,Indonesia,60111"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institut Teknologi Sepuluh Nopember,Faculty of Intelligent Electrical and Informatics Technology,Department of Electrical Engineering, Department of Computer Engineering,Surabaya,Indonesia,60111","institution_ids":["https://openalex.org/I166843116"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I166843116"],"apc_list":null,"apc_paid":null,"fwci":1.4284,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.8042465,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9879999756813049,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9682999849319458,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6045637726783752},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5309605002403259},{"id":"https://openalex.org/keywords/hippocampus","display_name":"Hippocampus","score":0.5070610046386719},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.46320924162864685},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4557258188724518},{"id":"https://openalex.org/keywords/net","display_name":"Net (polyhedron)","score":0.4300447404384613},{"id":"https://openalex.org/keywords/transfer","display_name":"Transfer (computing)","score":0.4170264005661011},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.22587502002716064},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.13675862550735474},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10695680975914001},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.08674892783164978},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.07264068722724915}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6045637726783752},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5309605002403259},{"id":"https://openalex.org/C2781161787","wikidata":"https://www.wikidata.org/wiki/Q48360","display_name":"Hippocampus","level":2,"score":0.5070610046386719},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.46320924162864685},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4557258188724518},{"id":"https://openalex.org/C14166107","wikidata":"https://www.wikidata.org/wiki/Q253829","display_name":"Net (polyhedron)","level":2,"score":0.4300447404384613},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.4170264005661011},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.22587502002716064},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.13675862550735474},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10695680975914001},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.08674892783164978},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.07264068722724915}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/civemsa58715.2024.10586572","is_oa":false,"landing_page_url":"https://doi.org/10.1109/civemsa58715.2024.10586572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2912321008","https://openalex.org/W1998607122","https://openalex.org/W2324368075","https://openalex.org/W2972124131","https://openalex.org/W338149487","https://openalex.org/W3201126466","https://openalex.org/W2972032537","https://openalex.org/W150363521","https://openalex.org/W3154107650","https://openalex.org/W2613671004"],"abstract_inverted_index":{"The":[0,77,140,169],"hippocampus,":[1],"a":[2,95,121,127,179],"crucial":[3,48],"component":[4],"of":[5,54,142],"the":[6,51,55,58,133,148,173],"human":[7],"brain,":[8],"is":[9,24,47,94],"involved":[10],"in":[11,84,106,115],"fundamental":[12],"cognitive":[13],"processes":[14],"such":[15],"as":[16],"learning,":[17,195],"memory,":[18],"and":[19,34,71,89,161,166],"spatial":[20,44],"navigation.":[21],"However,":[22],"it":[23],"susceptible":[25],"to":[26,73,131,176],"several":[27],"neuropsychiatric":[28],"disorders,":[29],"including":[30],"epilepsy,":[31],"Alzheimer\u2019s":[32],"disease,":[33],"depression.":[35],"Utilizing":[36],"Magnetic":[37],"Resonance":[38],"Imaging":[39],"(MRI)":[40],"techniques":[41],"with":[42,79,185],"efficient":[43],"navigation":[45],"capabilities":[46],"for":[49,97,102],"assessing":[50],"physiological":[52],"condition":[53],"hippocampus.":[56],"Labeling":[57],"hippocampus":[59,134],"on":[60,65],"MRI":[61,80],"images":[62],"primarily":[63],"depends":[64],"manual":[66],"methods,":[67],"which":[68,107,196],"are":[69,112,191],"time-consuming":[70],"prone":[72],"errors":[74],"between":[75],"observers.":[76],"issue":[78],"image":[81],"processing":[82],"lies":[83],"its":[85],"demanding":[86],"computational":[87,198],"requirements":[88],"lengthy":[90],"duration.":[91],"Furthermore,":[92],"there":[93],"need":[96],"more":[98],"three-dimensional":[99],"hippocampal":[100],"datasets":[101,111],"training":[103],"deep-learning":[104],"models,":[105],"3D":[108,122],"labeled":[109],"medical":[110,116],"often":[113],"scarce":[114],"imaging.":[117],"This":[118],"paper":[119],"introduces":[120],"U-Net":[123],"architecture":[124],"that":[125,147],"utilizes":[126],"transfer":[128,194],"learning":[129],"model":[130,138,150],"segment":[132],"from":[135,204],"different":[136],"pre-trained":[137],"scenarios.":[139],"results":[141,190],"all":[143],"test":[144],"scenarios":[145],"indicate":[146],"suggested":[149],"exhibits":[151],"an":[152],"average":[153],"Dice":[154],"Score,":[155,160],"Intersection":[156],"over":[157],"Union":[158],"(IoU)":[159],"Sensitivity":[162],"exceeding":[163],"0.85,":[164],"0.75,":[165],"0.80,":[167],"respectively.":[168],"proposed":[170],"methodology":[171],"enhances":[172],"model\u2019s":[174],"ability":[175],"generalize":[177],"within":[178],"shorter":[180],"timeframe,":[181],"even":[182],"when":[183],"dealing":[184],"limited":[186],"volumetric":[187],"datasets.":[188],"These":[189],"achieved":[192],"through":[193],"decreases":[197],"complexity":[199],"by":[200],"utilizing":[201],"pre-learned":[202],"characteristics":[203],"previous":[205],"tasks.":[206]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}